Pandas Profiling Report - Motor Insurance Claims Dataset

Dataset statistics

Number of variables	12
Number of observations	678013
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	44.0 MiB
Average record size in memory	68.0 B

Variable types

Numeric	8
Categorical	4

Alerts

`DrivAge` is highly correlated with `BonusMalus`	High correlation
`BonusMalus` is highly correlated with `DrivAge`	High correlation
`Area` is highly correlated with `Density` and 1 other fields	High correlation
`DrivAge` is highly correlated with `BonusMalus`	High correlation
`BonusMalus` is highly correlated with `DrivAge`	High correlation
`Density` is highly correlated with `Area` and 1 other fields	High correlation
`Region` is highly correlated with `Area` and 1 other fields	High correlation
`IDpol` has unique values	Unique
`ClaimNb` has 643953 (95.0%) zeros	Zeros
`VehAge` has 57739 (8.5%) zeros	Zeros

Reproduction

Analysis started	2022-03-02 08:16:41.898343
Analysis finished	2022-03-02 08:17:13.176074
Duration	31.28 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

IDpol
Real number (ℝ_≥0)

UNIQUE

Distinct	678013
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2621856.921

Minimum	1
Maximum	6114330
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	1
5-th percentile	69365.6
Q1	1157951
median	2272152
Q3	4046274
95-th percentile	6014195.2
Maximum	6114330
Range	6114329
Interquartile range (IQR)	2888323

Descriptive statistics

Standard deviation	1641782.753
Coefficient of variation (CV)	0.6261908266
Kurtosis	-0.6583474996
Mean	2621856.921
Median Absolute Deviation (MAD)	1152062
Skewness	0.2378901399
Sum	1.777653077 × 10¹²
Variance	2.695450607 × 10¹²
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2126260	1	< 0.1%
64613	1	< 0.1%
1100330	1	< 0.1%
3251083	1	< 0.1%
52131	1	< 0.1%
3160184	1	< 0.1%
3149256	1	< 0.1%
2017578	1	< 0.1%
3017221	1	< 0.1%
3240792	1	< 0.1%
Other values (678003)	678003	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	< 0.1%
3	1	< 0.1%
5	1	< 0.1%
10	1	< 0.1%
11	1	< 0.1%
13	1	< 0.1%
15	1	< 0.1%
17	1	< 0.1%
18	1	< 0.1%
21	1	< 0.1%

Value	Count	Frequency (%)
6114330	1	< 0.1%
6114329	1	< 0.1%
6114328	1	< 0.1%
6114327	1	< 0.1%
6114326	1	< 0.1%
6114325	1	< 0.1%
6114324	1	< 0.1%
6114323	1	< 0.1%
6114322	1	< 0.1%
6114321	1	< 0.1%

ClaimNb
Real number (ℝ_≥0)

ZEROS

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.05324676665

Minimum	0
Maximum	16
Zeros	643953
Zeros (%)	95.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	16
Range	16
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.2401173304
Coefficient of variation (CV)	4.509519461
Kurtosis	76.84187999
Mean	0.05324676665
Median Absolute Deviation (MAD)	0
Skewness	5.599613312
Sum	36102
Variance	0.05765633238
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	643953	95.0%
1	32178	4.7%
2	1784	0.3%
3	82	< 0.1%
4	7	< 0.1%
11	3	< 0.1%
5	2	< 0.1%
6	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	643953	95.0%
1	32178	4.7%
2	1784	0.3%
3	82	< 0.1%
4	7	< 0.1%
5	2	< 0.1%
6	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%
11	3	< 0.1%

Value	Count	Frequency (%)
16	1	< 0.1%
11	3	< 0.1%
9	1	< 0.1%
8	1	< 0.1%
6	1	< 0.1%
5	2	< 0.1%
4	7	< 0.1%
3	82	< 0.1%
2	1784	0.3%
1	32178	4.7%

Exposure
Real number (ℝ_≥0)

Distinct	187
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.5287501058

Minimum	0.00273224
Maximum	2.01
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	0.00273224
5-th percentile	0.04
Q1	0.18
median	0.49
Q3	0.99
95-th percentile	1
Maximum	2.01
Range	2.00726776
Interquartile range (IQR)	0.81

Descriptive statistics

Standard deviation	0.3644415463
Coefficient of variation (CV)	0.6892510136
Kurtosis	-1.524243691
Mean	0.5287501058
Median Absolute Deviation (MAD)	0.37
Skewness	0.08531780026
Sum	358499.4455
Variance	0.1328176407
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	168125	24.8%
0.08	44670	6.6%
0.07	12969	1.9%
0.24	12950	1.9%
0.5	12497	1.8%
0.49	12298	1.8%
0.03	11996	1.8%
0.04	11131	1.6%
0.12	11047	1.6%
0.2	8727	1.3%
Other values (177)	371603	54.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.00273224	295	< 0.1%
0.002732240437	765	0.1%
0.002739726	312	< 0.1%
0.002739726027	1733	0.3%
0.005464480874	464	0.1%
0.005464481	145	< 0.1%
0.005479452	355	0.1%
0.005479452055	1041	0.2%
0.008196721	113	< 0.1%
0.008196721311	507	0.1%

Value	Count	Frequency (%)
2.01	2	< 0.1%
2	1	< 0.1%
1.99	1	< 0.1%
1.98	1	< 0.1%
1.93	1	< 0.1%
1.92	1	< 0.1%
1.9	2	< 0.1%
1.88	1	< 0.1%
1.85	2	< 0.1%
1.82	1	< 0.1%

Area
Categorical

HIGH CORRELATION

Distinct	6
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	662.5 KiB

C	191880
D	151596
E	137167
A	103957
B	75459

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	D
2nd row	D
3rd row	B
4th row	B
5th row	B

Common Values

Value	Count	Frequency (%)
C	191880	28.3%
D	151596	22.4%
E	137167	20.2%
A	103957	15.3%
B	75459	11.1%
F	17954	2.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
c	191880	28.3%
d	151596	22.4%
e	137167	20.2%
a	103957	15.3%
b	75459	11.1%
f	17954	2.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

VehPower
Real number (ℝ_≥0)

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.454631401

Minimum	4
Maximum	15
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	4
5-th percentile	4
Q1	5
median	6
Q3	7
95-th percentile	11
Maximum	15
Range	11
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.050905698
Coefficient of variation (CV)	0.3177417222
Kurtosis	1.668206924
Mean	6.454631401
Median Absolute Deviation (MAD)	1
Skewness	1.17134444
Sum	4376324
Variance	4.206214181
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
6	148976	22.0%
7	145401	21.4%
5	124821	18.4%
4	115349	17.0%
8	46956	6.9%
10	31354	4.6%
9	30085	4.4%
11	18352	2.7%
12	8214	1.2%
13	3229	0.5%
Other values (2)	5276	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
4	115349	17.0%
5	124821	18.4%
6	148976	22.0%
7	145401	21.4%
8	46956	6.9%
9	30085	4.4%
10	31354	4.6%
11	18352	2.7%
12	8214	1.2%
13	3229	0.5%

Value	Count	Frequency (%)
15	2926	0.4%
14	2350	0.3%
13	3229	0.5%
12	8214	1.2%
11	18352	2.7%
10	31354	4.6%
9	30085	4.4%
8	46956	6.9%
7	145401	21.4%
6	148976	22.0%

VehAge
Real number (ℝ_≥0)

ZEROS

Distinct	78
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.044264638

Minimum	0
Maximum	100
Zeros	57739
Zeros (%)	8.5%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	6
Q3	11
95-th percentile	17
Maximum	100
Range	100
Interquartile range (IQR)	9

Descriptive statistics

Standard deviation	5.66623158
Coefficient of variation (CV)	0.804375172
Kurtosis	6.522053975
Mean	7.044264638
Median Absolute Deviation (MAD)	4
Skewness	1.147988998
Sum	4776103
Variance	32.10618032
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	71284	10.5%
2	59124	8.7%
0	57739	8.5%
3	50261	7.4%
4	43492	6.4%
5	38737	5.7%
10	38395	5.7%
6	35717	5.3%
7	32880	4.8%
8	32680	4.8%
Other values (68)	217704	32.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	57739	8.5%
1	71284	10.5%
2	59124	8.7%
3	50261	7.4%
4	43492	6.4%
5	38737	5.7%
6	35717	5.3%
7	32880	4.8%
8	32680	4.8%
9	31922	4.7%

Value	Count	Frequency (%)
100	25	< 0.1%
99	23	< 0.1%
85	1	< 0.1%
84	1	< 0.1%
83	2	< 0.1%
82	1	< 0.1%
81	3	< 0.1%
80	3	< 0.1%
79	1	< 0.1%
78	1	< 0.1%

DrivAge
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	83
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	45.4991217

Minimum	18
Maximum	100
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	18
5-th percentile	25
Q1	34
median	44
Q3	55
95-th percentile	72
Maximum	100
Range	82
Interquartile range (IQR)	21

Descriptive statistics

Standard deviation	14.13744407
Coefficient of variation (CV)	0.3107190545
Kurtosis	-0.3426884401
Mean	45.4991217
Median Absolute Deviation (MAD)	10
Skewness	0.4357585748
Sum	30848996
Variance	199.867325
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
36	17530	2.6%
38	17346	2.6%
39	17320	2.6%
37	17295	2.6%
52	17195	2.5%
34	17059	2.5%
40	17017	2.5%
51	17016	2.5%
41	16977	2.5%
42	16953	2.5%
Other values (73)	506305	74.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
18	748	0.1%
19	2392	0.4%
20	3676	0.5%
21	4437	0.7%
22	5291	0.8%
23	6261	0.9%
24	7393	1.1%
25	8697	1.3%
26	10301	1.5%
27	11827	1.7%

Value	Count	Frequency (%)
100	3	< 0.1%
99	70	< 0.1%
98	5	< 0.1%
97	10	< 0.1%
96	15	< 0.1%
95	24	< 0.1%
94	32	< 0.1%
93	55	< 0.1%
92	66	< 0.1%
91	121	< 0.1%

BonusMalus
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	115
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	59.76150162

Minimum	50
Maximum	230
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	50
5-th percentile	50
Q1	50
median	50
Q3	64
95-th percentile	95
Maximum	230
Range	180
Interquartile range (IQR)	14

Descriptive statistics

Standard deviation	15.63665766
Coefficient of variation (CV)	0.2616510167
Kurtosis	2.674811214
Mean	59.76150162
Median Absolute Deviation (MAD)	0
Skewness	1.728934068
Sum	40519075
Variance	244.5050628
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
50	384156	56.7%
100	19530	2.9%
68	18791	2.8%
72	18580	2.7%
76	18226	2.7%
64	18192	2.7%
80	18086	2.7%
57	17938	2.6%
60	17363	2.6%
54	17360	2.6%
Other values (105)	129791	19.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
50	384156	56.7%
51	15869	2.3%
52	4770	0.7%
53	3351	0.5%
54	17360	2.6%
55	5593	0.8%
56	3453	0.5%
57	17938	2.6%
58	5970	0.9%
59	2779	0.4%

Value	Count	Frequency (%)
230	1	< 0.1%
228	1	< 0.1%
218	1	< 0.1%
208	1	< 0.1%
198	2	< 0.1%
196	3	< 0.1%
195	6	< 0.1%
190	3	< 0.1%
187	3	< 0.1%
185	5	< 0.1%

VehBrand
Categorical

Distinct	11
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	662.6 KiB

B12	166024
B1	162736
B2	159861
B3	53395
B5	34753
Other values (6)	101244

Length

Max length	3
Median length	2
Mean length	2.314951188
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	B12
2nd row	B12
3rd row	B12
4th row	B12
5th row	B12

Common Values

Value	Count	Frequency (%)
B12	166024	24.5%
B1	162736	24.0%
B2	159861	23.6%
B3	53395	7.9%
B5	34753	5.1%
B6	28548	4.2%
B4	25179	3.7%
B10	17707	2.6%
B11	13585	2.0%
B13	12178	1.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
b12	166024	24.5%
b1	162736	24.0%
b2	159861	23.6%
b3	53395	7.9%
b5	34753	5.1%
b6	28548	4.2%
b4	25179	3.7%
b10	17707	2.6%
b11	13585	2.0%
b13	12178	1.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

VehGas
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	662.4 KiB

Regular	345877
Diesel	332136

Length

Max length	7
Median length	7
Mean length	6.510133287
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Regular
2nd row	Regular
3rd row	Diesel
4th row	Diesel
5th row	Diesel

Common Values

Value	Count	Frequency (%)
Regular	345877	51.0%
Diesel	332136	49.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
regular	345877	51.0%
diesel	332136	49.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Density
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	1607
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1792.422405

Minimum	1
Maximum	27000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.2 MiB

Quantile statistics

Minimum	1
5-th percentile	20
Q1	92
median	393
Q3	1658
95-th percentile	7313
Maximum	27000
Range	26999
Interquartile range (IQR)	1566

Descriptive statistics

Standard deviation	3958.646564
Coefficient of variation (CV)	2.208545571
Kurtosis	24.86945063
Mean	1792.422405
Median Absolute Deviation (MAD)	355
Skewness	4.65142115
Sum	1215285692
Variance	15670882.62
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
27000	10515	1.6%
3317	9891	1.5%
1313	7157	1.1%
9307	5986	0.9%
3744	5540	0.8%
1326	5447	0.8%
405	5195	0.8%
4128	5055	0.7%
4762	4985	0.7%
57	4262	0.6%
Other values (1597)	613980	90.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	7	< 0.1%
2	92	< 0.1%
3	304	< 0.1%
4	274	< 0.1%
5	438	0.1%
6	752	0.1%
7	1088	0.2%
8	1131	0.2%
9	1813	0.3%
10	2911	0.4%

Value	Count	Frequency (%)
27000	10515	1.6%
23396	66	< 0.1%
22821	182	< 0.1%
22669	463	0.1%
21410	76	< 0.1%
20000	6	< 0.1%
18229	200	< 0.1%
17140	910	0.1%
16533	613	0.1%
16291	175	< 0.1%

Region
Categorical

HIGH CORRELATION

Distinct	22
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	663.0 KiB

R24	160601
R82	84752
R93	79315
R11	69791
R53	42122
Other values (17)	241432

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	R82
2nd row	R82
3rd row	R22
4th row	R72
5th row	R72

Common Values

Value	Count	Frequency (%)
R24	160601	23.7%
R82	84752	12.5%
R93	79315	11.7%
R11	69791	10.3%
R53	42122	6.2%
R52	38751	5.7%
R91	35805	5.3%
R72	31329	4.6%
R31	27285	4.0%
R54	19046	2.8%
Other values (12)	89216	13.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
r24	160601	23.7%
r82	84752	12.5%
r93	79315	11.7%
r11	69791	10.3%
r53	42122	6.2%
r52	38751	5.7%
r91	35805	5.3%
r72	31329	4.6%
r31	27285	4.0%
r54	19046	2.8%
Other values (12)	89216	13.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	IDpol	ClaimNb	Exposure	Area	VehPower	VehAge	DrivAge	BonusMalus	VehBrand	VehGas	Density	Region
0	1.0	1	0.10	D	5	0	55	50	B12	Regular	1217	R82
1	3.0	1	0.77	D	5	0	55	50	B12	Regular	1217	R82
2	5.0	1	0.75	B	6	2	52	50	B12	Diesel	54	R22
3	10.0	1	0.09	B	7	0	46	50	B12	Diesel	76	R72
4	11.0	1	0.84	B	7	0	46	50	B12	Diesel	76	R72
5	13.0	1	0.52	E	6	2	38	50	B12	Regular	3003	R31
6	15.0	1	0.45	E	6	2	38	50	B12	Regular	3003	R31
7	17.0	1	0.27	C	7	0	33	68	B12	Diesel	137	R91
8	18.0	1	0.71	C	7	0	33	68	B12	Diesel	137	R91
9	21.0	1	0.15	B	7	0	41	50	B12	Diesel	60	R52

Last rows

	IDpol	Exposure	Area	VehPower	VehAge	DrivAge	BonusMalus	VehBrand	VehGas	Density	Region
678003	6114321.0	0.005479	E	4	0	29	80	B12	Regular	5360	R11
678004	6114322.0	0.005479	E	11	0	49	74	B12	Diesel	5360	R11
678005	6114323.0	0.005479	D	4	0	34	80	B12	Regular	731	R82
678006	6114324.0	0.005479	D	11	0	41	50	B12	Diesel	528	R93
678007	6114325.0	0.005479	E	6	4	40	68	B12	Regular	2733	R93
678008	6114326.0	0.002740	E	4	0	54	50	B12	Regular	3317	R93
678009	6114327.0	0.002740	E	4	0	41	95	B12	Regular	9850	R11
678010	6114328.0	0.002740	D	6	2	45	50	B12	Diesel	1323	R82
678011	6114329.0	0.002740	B	4	0	60	50	B12	Regular	95	R26
678012	6114330.0	0.002740	B	7	6	29	54	B12	Diesel	65	R72

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows